Project Summary The aim of this proposal is to hold an annual, full-week course in advanced gene mapping at The Rockefeller University in New York. The course is directed towards advanced researchers who are familiar with the fundamental aspects of statistical genetics but who need to become more proficient in the analysis of complex traits. Thirty-four students will be accepted to each course, and stipends, to cover travel costs, will be provided to ten participants who are either pre-doctoral students or post-doctoral fellows. The course consists of two components: lectures on important current topics in gene mapping, as well as hands-on exercises to be performed with the latest freeware software programs using cloud computing. The current emphasis of the course is analyses of sequence and other omics data. The next Advanced Gene Mapping course will be held January 27-31, 2020. The PI and five additional instructors who are experts in their respective fields will teach on a variety of topics that include: data quality control; qualitative and quantitative trait (population and family-based data) association analysis of whole-genome data (genotype, sequence, and imputed); controlling for population admixture and substructure; meta-analysis; sample size estimation and power calculations; detecting gene x gene interactions; analysis of epigenomic and RNAseq data; elucidating pleiotropy; functional prediction and variant annotation; estimation of polygenic risk scores; Mendelian randomization and mediation analysis; and fine mapping. A variety of freely available software including ANNOVAR, GCTA-MLMA, PAINTOR, LDpred, PrediXCan, R, SEQSpark, and VAT will be implemented to perform practical exercises. Since gene mapping is a quickly changing field, the topics and analytic programs will be updated annually to reflect the latest developments in the field of statistical genetics. Given the vast amounts of generated genetic data, it is essential to train researchers and give them the necessary information and tools for data analysis to elucidate the etiology of complex traits.